Eran Raichstein is a seasoned engineering leader and researcher with 11+ years at IBM driving networking and server virtualization R&D and prior CTO experience in backup/replication products. He combines deep expertise in storage, communication and I/O technologies with hands-on product design, field testing and customer-facing risk analysis. At IBM he leads strategy for large-scale virtualized and multi-tenant environments while previously steering FilesX and TSM FastBack architectures end-to-end. Eran is also an active contributor to open-source tooling around log parsing and OpenShift logging, adding Kafka persistence, masking and robust testing to production-focused projects. Based in Haifa and trained at Technion, he blends defense-grade embedded firmware experience from Elbit with enterprise storage and cloud-native virtualization know-how. Colleagues rely on him for pragmatic technical vision that bridges research, product delivery and operational realities.
11 years of coding experience
16 years of employment as a software developer
Bachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at Technion-Machon Technologi Le' Israel
Operator to support logging subsystem of OpenShift
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:75 reviews, 6 commits, 13 PRs in 3 months
Contributions summary:Eran primarily contributed to the testing framework and functional tests within the `openshift/cluster-logging-operator` repository. Their commits include adding and modifying test cases, such as those for application and audit log formats. The changes also included adding functionality to check if the elastic search container is ready. The user's contributions are centered around ensuring the correct parsing and forwarding of log data within the OpenShift logging system.
A robust streaming log template miner based on the Drain algorithm
Role in this project:
Back-end Developer
Contributions:11 commits, 7 PRs, 2 pushes in 14 days
Contributions summary:Eran primarily focused on enhancing the persistent log parser, `drain3`. They implemented support for Kafka persistence, including configuration and restoration, alongside file-based persistence. The user added features for masking log content and improved snapshotting mechanisms. Furthermore, the user addressed configuration management, implemented logging, and updated the project's version and dependencies, demonstrating an iterative approach to improve the project.
drainpythonclusterstemplate-miningpython3
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Eran Raichstein - STSM, Manager, Networking And Virtualization Technologies Research Group